Improved Algorithms for Data Migration


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1 Improved Algorithms for Data Migration Samir Khuller 1, YooAh Kim, and Azarakhsh Malekian 1 Department of Computer Science, University of Maryland, College Park, MD Research supported by NSF Award CCF Department of Computer Science and Engineering, University of Connecticut, Storrs, CT Abstract. Our work is motivated by the need to manage data on a collection of storage devices to handle dynamically changing demand. As demand for data changes, the system needs to automatically respond to changes in demand for different data items. The problem of computing a migration plan among the storage devices is called the data migration problem. This problem was shown to be NP hard, and an approximation algorithm achieving an approximation factor of 9.5 was presented for the halfduplex communication model in [Khuller, Kim and Wan: Algorithms for Data Migration with Cloning, SIAM J. on Computing, Vol. 33(2): (2004)]. In this paper we develop an improved approximation algorithm that gives a bound of o(1) using various new ideas. In addition, we develop better algorithms using external disks and get an approximation factor of 4.5. We also consider the full duplex communication model and develop an improved bound of 4 + o(1) for this model, with no external disks. 1 Introduction To handle high demand, especially for multimedia data, a common approach is to replicate data objects within the storage system. Typically, a large storage server consists of several disks connected using a dedicated network, called a Storage Area Network. Disks typically have constraints on storage as well as the number of clients that can access data from a single disk simultaneously. These systems are getting increasing attention since TV channels are moving to systems where TV programs will be available for users to watch with full video functionality (pause, fast forward, rewind etc.). Such programs will require large amounts of storage, in addition to bandwidth capacity to handle high demand. Approximation algorithms have been developed [16, 17, 7, 11] to map known demand for data to a specific data layout pattern to maximize utilization, where the utilization is the total number of clients that can be assigned to a disk that contains the data they want. In the layout, we compute not only how many copies of each item we need, but also a layout pattern that specifies the precise subset of items on each disk. The problem is NP hard, but there are polynomialtime approximation schemes [7, 16, 17, 11]. Given the relative demand for data, the
2 algorithm computes an almost optimal layout. Note that this problem is slightly different from the data placement problem considered in [9, 15, 3] since all the disks are in the same location, it does not matter which disk a client is assigned to. Even in this special case, the problem is NP hard [7]. Over time as the demand for data changes, the system needs to create new data layouts. The problem we are interested in is the problem of computing a data migration plan for the set of disks to convert an initial layout to a target layout. We assume that data objects have the same size (these could be data blocks, or files) and that it takes the same amount of time to migrate any data item from one disk to another disk. In this work we consider two models. In the first model (halfduplex) the crucial constraint is that each disk can participate in the transfer of only one item either as a sender or as a receiver. In other words, the communication pattern in each round forms a matching. Our goal is to find a migration schedule to minimize the time taken to complete the migration (makespan). To handle high demand for popular objects, new copies will have to be dynamically created and stored on different disks. All previous work on this problem deals with the halfduplex model. We also consider the fullduplex model, where each disk can act as a sender and a receiver in each round for a single item. Previously we did not consider this natural extension of the halfduplex model since we did not completely understand how to utilize its power to prove interesting approximation guarantees. The formal description of the data migration problem is as follows: data item i resides in a specified (source) subset S i of disks, and needs to be moved to a (destination) subset D i. In other words, each data item that initially belongs to a subset of disks, needs to be moved to another subset of disks. (We might need to create new copies of this data item and store it on an additional set of disks.) See Figure 1 for an example. If each disk had exactly one data item, and needs to copy this data item to every other disk, then it is exactly the problem of gossiping. The data migration problem in this form was first studied by Khuller, Kim and Wan [4], and it was shown to be NPhard. In addition, a polynomialtime 9.5approximation algorithm was developed for the halfduplex communication model. A slightly different formulation was considered by Hall et al. [10] in which a particular transfer graph was specified. While they can solve the problem very well, this approach is limited in the sense that it does not allow (a) cloning (creation of several new copies) and (b) does not allow optimization over the space of transfer graphs. In [4] it was shown that a more general problem formulation is the one with source and destination subsets specified for each data item. However, the main focus in [10] is to do the transfers without violating space constraints. Another formulation has been considered recently where one can optimize over the space of possible target layouts [12]. The resulting problems are also N P hard. However, no significant progress on developing approximation algorithms was made on this problem. A simple flow based heuristic was presented for the problem, and was demonstrated to be effective in finding good target layouts. 2
3 Job migration has also been considered in the scheduling context recently as well [2], where a fixed number of jobs can be migrated to reduce the makespan by as much as possible. There is a lot of work on data migration for minimizing completion time for a fixed transfer graph as well (see [6, 14] for references). Fig. 1. An initial and target layout, and their corresponding S i s and D i s. For example, disk 1 initially has items {2, 4, 5} and in the target layout has items {1, 3, 4}. 1.1 Communication Model Different communication models can be considered based on how the disks are connected. In this paper we consider two models. The first model is the same model as in the work by Hall et al. [10, 1, 4, 13] where the disks may communicate on any matching; in other words, the underlying communication graph allows for communication between any pair of devices via a matching (a switched storage network with unbounded backplane bandwidth). Moreover, to model the limited switching capacity of the network connecting the disks, one could allow for choosing any matching of bounded size as the set of transfers that can be done in each round. We call this the boundedsize matching model. It was shown in [4] that an algorithm for the bounded matching model can be obtained by a simple simulation of the algorithm for the unbounded matching model with excellent performance guarantees. In addition we consider the full duplex model where each disk may act as a sender and a receiver for an item in each round. Note that we do not require the communication pattern to be a matching any more. For example, we may have cycles, with disk 1 sending an item to disk 2, disk 2 to disk 3 and disk 3 to disk 1. In earlier work we did not discuss this model as we were unable to utilize the power of this model to prove nontrivial approximation guarantees. Note that this does not correspond directly to edge coloring anymore. 1.2 Our Results Our approach is based on the approach initially developed in [4]. Using various new ideas lets us reduce the approximation factor to 6.5+o(1). The main technical difficulty is simply that of putting it all together and making the analysis work. In addition we show two more results. If we are allowed to use external disks (called bypass disks in [10]), we can improve the approximation guarantee further to max(3, γ). This can be achieved by using at most γ external disks, where is the number of items that need to be migrated. We assume that each external disk can hold γ items. This gives an approximation factor of 4.5 by setting γ = 3. 3
4 Finally, we also consider the fullduplex model where each disk can be the source or destination of a transfer in each round. In this model we show that an approximation guarantee of 4 + o(1) can be achieved. Earlier, we did not focus on this model specifically as we were unable to utilize the power of this model in any nontrivial manner. The algorithm developed in [4] has been implemented, and we performed an extensive set of experiments comparing its performance with the performance of other heuristics [8]. Even though the worst case approximation factor is 9.5, the algorithm performed very well in practice, giving approximation ratios within twice the optimal solution in most cases. 2 The Data Migration Algorithm We start this section by describing some theorems from the edge coloring and scheduling literature and also the lower bounds that we will use in the following sections for analysis. In the second part, we present our suggested data migration algorithm. 2.1 Preliminaries Our algorithms make use of known results on edge coloring of multigraphs. Given a graph G with max degree G and multiplicity µ the following results are known (see BondyMurty [5] for example). Let χ be the edge chromatic number of G. Note that when G is bipartite, χ = G and such an edge coloring can be obtained in polynomial time [5]. Theorem 1. (Vizing [20]) If G has no selfloops then χ G + µ. Theorem 2. (Shannon [18]) If G has no selfloops then χ 3 2 G. Another result that we will use (related to scheduling) is the following theorem by shmoys and Tardos: Theorem 3. (ShmoysTardos [19]) We are given a collection of jobs J, each of which is to be assigned to exactly one machine among the set M; if job j J is assigned to machine i M, then it requires p ij units of processing time, and incurs a cost c ij. Suppose that there exists a fractional solution (that is, a job can be assigned fractionally to machines) with makespan P and total cost C. Then in polynomial time we can find a schedule with makespan P + max p ij and total cost C. We are using two main lowerbounds for our analysis. As in [4] let β j be {i j D i }, i.e., the number of different sets D i, to which a disk j belongs. We then define β as max j=1...n β j. In other words, β is an upper bound on the number of items a disk may need. Note that β is a lower bound on the optimal number of rounds, since the disk j that attains the maximum, needs at least β rounds to receive all the items i such that j D i, since it can receive at most 4
5 one item in each round. Another lower bound that we will use in the analysis is α which is defined as follows: For an item i decide a primary source s i S i so that α = max j=1,...,n ( {i j = s i } + β j ) is minimized. In other words, α is the maximum number of items for which a disk may be a primary source (s i ) or destination. As one can see α is also a lower bound on the optimal number of rounds. Moreover, we may assume that D i and D i S i =. This is because we can define the destination set D i as the set of disks that need item i and do not currently have it. Next, we present a high level description of our suggested data migration algorithm. 2.2 Data Migration Algorithm: High Level Idea The high level idea of the algorithm can be described as follows: Algorithm 4 1. For each item i, find a disk call it the primary source s i S i such that max j=1,...,n ( {i j = s i } + β j ) is minimized. Later we show how we can do this step in polynomial time. 2. For each item i, we define two different subgroups G i D i and R i ( D i ) with the following properties: G i s are disjoint from each other and at first, we send item i to these disks. R i sets are not disjoint from each other but each disk belongs to only a bounded number of different R i sets. In our algorithm, we send data from G i to R i and then from R i to the rest of the disks in D i. 3. R i sets are selected as follows: (a) First partition D i into subgroups D i,k k = 0... Di q of size at most q(q is a parameter that will be specified later.) That is, we partition D i into Di q subgroups of size q and possibly one subgraph of size less than q (if D i is not a multiple of q). (b) Now select R i D i and assign D i,k subsets to a disk in R i such that for each disk in R i the total size of subgroups assigned to the disk is at most β + q. (We will see later that it is always possible to select R i with this property.) Let r i be the disk in R i to which the small subgroup (a subgroup with size strictly less than q) is assigned. Note that if D i is a multiple of q, there is no disk r i. We define R i to be R i \ r i. 4. Compute G i D i such that G i = Di β and they are mutually disjoint. 5. For each item i for which G i = but R i, we select a disk g i. Let G i = G i if G i is not empty and G i = {g i} otherwise. Note that g i disk exists iff q < D i < β. 6. Send data item i from the primary source s i to G i. 5
6 7. Send item i from G i to R i \ G i by setting up a transfer graph and using an edge coloring to synchronize the transfer. Here, R i is defined to be R i \ r i where r i is the disk in R i to which the small subgroup (a subgroup with size strictly less than q) is assigned. 8. Send item i from s i to r i if r i has not received item i. 9. Finally set up a transfer graph from R i to D i \ R i. We find an edge coloring of the transfer graph and the number of colors used is an upper bound on the number of rounds required to ensure that each disk in D i gets item i. In Lemma 7 we derive an upper bound on the number of required colors. In the previous approach given in [4], first we chose disjoint representative sets in D i (equivalent to G i sets here)and items are first migrated to these subsets and then from these subsets to the rest of the disks in need of each item. By choosing disjoint sets, broadcasting inside the subsets are faster and easier but also disjointness limits the size of the subsets and that would cause the increase in the number of rounds in the following phase. Here, we add new representative sets (R i ) that are not necessarily disjoint. We still use disjoint subsets to broadcast to these sets and then we send data from the new representative sets (R i ) to the remaining disks. In the following sections, we will describe the details of the algorithm 4. 3 Step Details We present the step details in the same order that was described in the algorithm 4. Step 1: Selecting the primary source for each item. This is exactly the same as Lemma 3.1 described in [4]. Lemma 1. [4] We can find a source s i S i for each item i so that max j=1...n ( {i j = s i } + β j ) is minimized, using a flow network. Proof. We create a flow network with a source s and a sink t as shown in Figure??. We have two set of nodes corresponding to disks and items. Add directed edges from s to nodes for items and also directed edges from item i to disk j if j S i. The capacities of all those edges are one. Finally we add an edge from the node corresponding to disk j to t with capacity α β j. We want to find the minimum α so that the maximum flow of the network is. We can do this by checking if there is a flow of with α starting from max β j and increasing by one until it is satisfied. If there is outgoing flow from item i to disk j, then we set j as s i. Step 3: Selecting R i for each item i. Let D ik (k = 1,..., Di q ) be kth subgroup of D i. The size of D ik is q for k = 1,..., Di q and D ik, k = Di q + 1 contains the remaining disks in D i of size D i q D i /q (and it could be possibly empty). To show how we assign D ij to R i we use Theorem 3. In our problem, 6
7 we can think of each subgroup D ik as a job and each disk as a machine. If disk j belongs to D i, then we can assign job D ik to disk j with zero cost. The processing time is the size of D ik, which is at most q. If disk j does not belongs to D i, then the cost to assign D ik to j is (disk j cannot be in R i ). First we can show that: Lemma 2. There exists a fractional assignment such that the max load of each disk is at most β. Proof. We can assign 1 D i fraction of subgroup D ik to each disk j D i. It is easy to check that every subgroup D ik is completely assigned. The load on disk j is given by i:j D i Now we can show that: k D ik D i = i:j D i 1 D i D ik = k i:j D i 1 β Lemma 3. There is a way to choose R i sets for each i = 1... and assign subgroups D ik such that for each disk in R i the total size of subgroups D ik assigned to the disk is at most β + q. Proof. By Theorem 3, we can convert the fractional solution obtained in Lemma 2 to an integral solution such that each subgroup is completely assigned to one disk, and the maximum load on a disk is at most β + q. ( Since as maximum size of D ik is q.) Considering this assignment, we can directly conclude that: Fact. For each disk j, at most β/q + 1 different large subgroups D ik (of size exactly q) can be assigned to the disk j. The reason is that the number of disks assigned to R i is at most β + q and the size of each large subgroup is q. We will use this fact later. Step 4: Select G i D i. We can find disjoint sets G i D i using the same algorithm as in [4]. For completeness we include their method here: Lemma 4. There is a way to choose disjoint sets G i for each i = 1..., such that G i = Di β and G i D i. Proof. Create a flow network with a source s and sink t. In addition we have two sets of vertices U and W. The first set U has nodes, each corresponding to a disk that is the source of an item. The set W has N nodes, each corresponding to a disk in the system. We add directed edges from s to each node in U, such that the edge (s, i) has capacity Di β. We also add directed edges with infinite capacity from node i U to j W if j D i. We add unit capacity edges from s to t in this network. The mincut in this network is obtained by simply selecting the outgoing edges from s. We can find a fractional flow of this value as follows: 7
8 saturate all the outgoing edges from s. From each node i there are D i edges to nodes in W. Suppose Γ i = Di β. Send 1 β units of flow along Γ iβ D i this can be done. Observe that the total incoming flow to a vertex in W is at most 1 since there are at most β incoming edges, each carrying at most 1 β units of flow. An integral max flow in this network will correspond to G i units of flow going from s to i, and from i to a subset of vertices in D i before reaching t. The vertices to which i has nonzero flow will form the set G i. The above approach can help us find G i sets. However if G i = but R i, we need to select another disk g i as well. Note that if G i = 0 then D i < β, and therefore, R i < β q. We define G i to be G i if G i and G i = g i otherwise. Next, we show how to select g i as well. Lemma 5. For each item i for which G i = but R i, we can find g i so that for a disk j, i:j=g i R i 2 β q + 1. Or in other words, each g i is responsible for at most 2β q + 1 disks. Proof. We again use Theorem 3. Reduce the problem to the following scheduling problem: Consider each disk as a machine. For each item such that G i = 0, create a job of size R i. The cost of assigning job i to disk j is 1 iff j R i, otherwise it is infinite. Note that there is a fractional assignment such that the load on each disk(machine) is at most β q + 1. The way to show it is by assigning 1 R i fraction of each job to each machine (disk) in its R i set. The load due to this job on the machine (disk) is 1. Since a disk is in at most β q + 1 different R i sets (based on the fact given in previous section), the fractional load on each machine(disk) is at most β q + 1. By applying the ShmoysTardos [19] scheduling algorithm (Theorem 3), we can find an assignment of jobs (items) to machines (disks) such that the total cost is at most the number of items and the load on each machine (disk) is at most 2β q + 1. Note that the size of each job is at most β q.) g i will be the disk(machine) that item i is assigned to. Step 6: Sending items from S i to G i First we show how to send data from S i to G i and also give the number of rounds these transfers take. We claim that this can be done in 2OP T + O( β q ) rounds. We develop a lower bound on the optimal solution by solving the following linear program L(m) for a given m. L(m) : m n ijk x ijk G i for all i (1) j k=1 x ijk 1 for all j, k (2) i 0 x ijk 1 (3) where n ijk = min(2 m k, G i ) if disk j belongs to S i and n ijk = 0 otherwise. Intuitively, x ijk indicates that at time k, disk j send item i to some disk in 8
9 G i. Let M be the minimum m such that L(m) has a feasible solution. Note that M is a lowerbound for the optimal solution. (Otherwise, consider a feasible migration and set x ijk based on the given schedule as defined above). One can easily verify that the schedule gives a feasible solution for the linear program L(m). Also, we know that between all the feasible solutions, M is the smallest possible m that has a feasible solution. Now, we show that: Lemma 6. We can perform migrations from S i to G i in 2 M +O(β/q) rounds. Proof. Given a fractional solution x to L(M), we can obtain an integral solution x such that for all i, j k x ijk j k x ijk.(using Lemma 3.4 from [4]). For each item i, we arbitrarily select min( j k x ijk, G i ) disks from G i. Let H i denote this subset. We create the following transfer graph from S i to H i : create an edge from a disk j S i to a disk H i if x ijk = 1. (Make sure every disk in H i has an incoming edge from a disk in S i.) Note the indegree of a disk in this transfer graph is 2+ β q since a disk can belong to H i for at most 2+β/q different i s (a disk can be g i for at most β/q + 1 different items and also may belong to one G i.) The outdegree is M and the multiplicity is 2β/q + 4. Therefore, we can perform the migration from S i to H i in M + O(β/q) rounds. For i with G i = 1, the transfer is complete. For the rest of the items, since sets G i (= G i) are disjoint, we can double the number of copies in each round until the number of copies becomes G i. After M rounds, the number of copies we can make for item i is at least 2 M H i = 2 M min( x ijk, G i ) j k min(2 M 1 2 x ijk, G i ) j k min(2 M 1 ( x ijk + 1), G i ) j k min(2 M 1 x ijk, G i ) j k min( n ijk x ijk, G i ) G i. j k k x The second inequality comes from the fact that j ijk 1. Therefore we can finish the whole transfer from S i to G i in 2 M + O(β/q) rounds. Step 7: Sending item i from G i to R i. We now focus on sending item i from the disks in G i to disks in R i. We construct a transfer graph to send data from G i to R i sets so that each disk in R i \ G i receives item i from one disk in G i. We create the transfer graph as follows: First, add directed edges from disks in G i to disks in R i. Recall that G i = Di β and R i = Di q. Since G i sets are disjoint, there is a transfer graph in which each disk in G i has at most Θ(β/q) outgoing edges. For items with G i =, we put edges from g i to all disks in R i. 9
10 The outdegree of each disk can be increased by at most 2 β q + 1. The indegree of a disk in R i is at most β q + 1 and the multiplicity is 2β q + 2. Therefore, this step can be done in O(β/q) rounds. Step 8: Sending item i from s i to r i. Again we create a transfer graph in which there is an edge from s i to r i if r i has not received item i in the previous steps. The indegree of a disk j is at most β j since a disk j is selected as r i only if j D i and the outdegree of disk j is at most α β j. Using Theorem 2, this step can be done in 3α 2 rounds. Step 9: Sending item i from R i to D i \ (R i G i ). We now create a transfer graph from R i to D i \ (R i G i ) such that there is an edge from disk a R i to disk b if the subgroup that b belongs to is assigned to a in Lemma 3. We find an edge coloring of the transfer graph. The following lemma gives an upper bound on the number of rounds required to ensure that each disk in D i gets item i. Lemma 7. The number of colors we need to color the transfer graph is at most 3β + q. Proof. First, we compute the maximum indegree and outdegree of each node. The outdegree of a node is at most β + q due to the way we choose R i (See Lemma 3). The indegree of each node is at most β since in the transfer graph we send items only to the disks in their corresponding destination sets. Multiplicity of the graph is also at most β since we send data item i from disk j to disk k (or vice versa) only if both disk j and k belong to D i. By Theorem 1, we see that the maximum number of colors needed is at most 3β + q. To wrap up, in the next theorem we show that the total number of rounds in this algorithm is bounded by 6.5+o(1) times the optimal solution. Theorem 1. The total number of rounds required for the data migration is at most o(1) times OP T. Proof. The total number of rounds we need is 2M +3α/2+3β+O(β/q)+q. Since M, α, and β are the lowerbounds on the optimal solution, chooosing q = Θ( β) gives the desired result. 4 External Disks Until now we assumed that we had N disks, and the source and destination sets were chosen from this set of disks and only essential transfers are performed. In other words, if an item i is sent to disk j, then it must be that j D i (disk j was in the destination set for item i), hence the total number of transfers done is the least possible. In several situations, we may have access to idle disks with available storage that we can make use of as temporary devices to enable a faster completion of the transfers we are trying to schedule. In addition, we exploit the 10
11 fact that by performing a small number of nonessential transfers (this was also used in [13, 10]), we can further reduce the total number of rounds required. We show that indeed such techniques can considerably reduce the total number of rounds required for performing the transfers from S i sets to D i sets. We assume that each external disk has enough space to pack γ items. If we are allowed to use γ external disks, the approximation ratio can be improved to 3 + max(1.5, γ 2 ). For example, choosing γ = 3 gives a bound of 4.5. Define β = D i i=1 N. We can see that 2 β is a lowerbound on the optimal number of rounds since in each round at most N 2 data items can be transferred. The high level description of the algorithm is as follows: 1. Assign γ items to each external disk. Send items to their assigned external disks. 2. For each item i, choose disjoint G i sets of size Di β. 3. Send item i to all disks in the G i set. 4. Send item i from the G i set to all the disks in D i. We will also make use of the copy of item i on the external disk. We now discuss the steps in detail. First step can be done in at most max(α, γ) rounds by sending the items from their primary sources to the external disks (for this step we will compute α as before, with the change that we can ignore the β j term). The maximum degree of each disk is at most max(α, γ). Since the graph is bipartite, transferring items to their assigned external disks can be finished in max(α, γ) rounds. We can easily choose disjoint set G i as we are allowed to perform nonessential transfers (i.e., a disk j can belong to G i even if j is not in D i.) Hence we can use a simple greedy method to choose G i. Broadcasting items inside G i can be done in 2M rounds as described in Section 2. Next step is to send the item to all the remaining disks in the D i sets. We make a transfer graph as follows: assign to each disk in G i at most β disks in D i so that each disk in D i is assigned to at most one disk in the G i set. The number of unassigned disks from each D i set is at most β. Assign all of the remaining disks from D i to the external disk containing that item. The outdegree of the internal disks is at most β since each disk belongs to at most one G i set. The indegree of each internal disk is at most β since a disk will receive an item only if it is in its demand set. The multiplicity between two internal disks is at most 2. (Since each disk can belong to at most one G i set.) So the total degree of each internal disk is at most β + β. Each external disk has at most γ items and the number of remaining disks for each item is at most β. So the outdegree of each external disk is at most γ β γ 2 OP T. So the maximum degree of each node in the whole graph is at most max(β + β, γ β). and the maximum number of colors needed to color this graph is 1 2 max(3, γ)op T + max(2, γ). Adding up all these values the complete transfer can be done in α + 2m max(3, γ)op T + max(2, γ) ( max(3, γ))op T + 2γ + O(1). 11
12 5 Full Duplex Model In this section we consider the full duplex communication model. In this model, we assume that each disk can send and receive at most one item in each round. In the halfduplex model, we assumed that at each round, a disk can either send or receive one item (but not both at the same time). In the full duplex model the communication pattern does not have to induce a matching since directed cycles are allowed (the direction indicates the data transfer direction). We develop a 4+o(1) approximation algorithm for this model. In this model, given a transfer graph G, we find an optimal migration schedule for G as follows: Construct a bipartite graph by putting one copy of each disk in each partition. We call the copy of vertex u in the first partition u A, and in the other partition u B. We add an edge from u A to v B in the bipartite graph if and only if there is a directed edge in the transfer graph from u to v. The bipartite graph can be colored optimally in polynomial time and the number of colors is equal to the maximum degree of the bipartite graph. Note that β and M are still lower bounds on the optimal solution in the fullduplex model. The algorithm is the same as in Section 2 except the procedure to select primary sources s i. For each item i, decide a primary source s i so that α = max j=1...n (max( {j j = s i }, β j )) is minimized. Note that α is also a lower bound for the optimal solution. We can find these primary sources as shown in Lemma 8 by adapting the method used in [4]. We show how to find the primary sources s i. Lemma 8. By using network flow we can choose primary sources to minimize max j=1...n (max( {j j = s i }, β j )) Fig. 2. Computing α. Proof. Create two vertices s and t. (See Figure 5 for example.) Make two sets, one for the items and one for the disks. Add edges from s to each node corresponding to an item of unit capacity. Add a directed edge of infinite capacity between item j and disk i if i S j. Add edges of capacity α from each node in the set of disks to t. Find the minimum α (initially α = β), so that we can find a feasible flow of value. For each item j, choose the disk as its primary source s j to which it sends one unit of flow. Theorem 1. There is a 4 + o(1) approximation algorithm for data migration in the full duplex model. 12
13 Proof. Step 1 (from S i to G i ) and Step 2 (from G i to R i) still take 2M +O(β/q) rounds and O(β/q) rounds, respectively. For Step 3, if we construct a bipartite graph, then the max degree is at most max(α, β), which is the number of rounds required for this step. For Step 4, the maximum degree of the bipartite graph is β + q. Therefore, the total number of rounds we need is 2M + max(α, β) + β + O(β/q) + q. By choosing q = Θ( β), we can obtain a 4 + o(1)approximation algorithm. References 1. E. Anderson, J. Hall, J. Hartline, M. Hobbes, A. Karlin, J. Saia, R. Swaminathan and J. Wilkes. An Experimental Study of Data Migration Algorithms. Workshop on Algorithm Engineering, pages , London, UK, SpringerVerlag 2. G. Aggarwal, R. Motwani and A. Zhu. The load rebalancing problem. Symp. on Parallel Algorithms and Architectures, pages , (2003). 3. I. D. Baev and R. Rajaraman. Approximation algorithms for data placement in arbitrary networks. Proc. of ACMSIAM SODA, pp , S. Khuller, Y.A. Kim and Y.C. Wan. Algorithms for Data Migration with Cloning, Siam J. on Comput., Vol. 33, No. 2, pp ,Feb J. A. Bondy and U. S. R. Murty. Graph Theory with Applications. American Elsevier, New York, R. Gandhi and J. Mestre. Combinatorial algorithms for Data Migration to minimize the average completion time. APPROX (2006) (to appear). 7. L. Golubchik, S. Khanna, S. Khuller, R. Thurimella and A. Zhu. Approximation Algorithms for Data Placement on Parallel Disks. Proc. of ACMSIAM SODA, pages , Washington, D.C., USA, Society of Industrial and Applied Mathematics. 8. L. Golubchik, S. Khuller, Y. Kim, S. Shargorodskaya and Y. C. Wan. Data migration on parallel disks. Proc. of European Symp. on Algorithms (2004). LNCS 3221, pages Springer. To appear in Special Issue of Algorithmica from ESA S. Guha and K. Munagala. Improved algorithms for the data placement problem, Proc. of ACMSIAM SODA, pages , San Fransisco, CA, USA, Society of Industrial and Applied Mathematics. 10. J. Hall, J. Hartline, A. Karlin, J. Saia and J. Wilkes. On Algorithms for Efficient Data Migration. Proc. of ACMSIAM SODA, pp , S. Kashyap and S. Khuller. Algorithms for NonUniform Size Data Placement on Parallel Disks. Conference on FST&TCS Conference, LNCS 2914, pp , Full version to appear in Journal of Algorithms (2006). 12. S. Kashyap, S. Khuller, Y. C. Wan and L. Golubchik. Fast reconfiguration of data placement in parallel disks ALENEX Conference, Jan S. Khuller, Y. Kim and Y. C. Wan. On Generalized Gossiping and Broadcasting. ESA Conference. pages , Budapest, Hungary, Springer. 14. Y. Kim. Data Migration to minimize the average completion time. Proc. of ACM SIAM SODA, pp , A. Meyerson, K. Munagala, and S. A. Plotkin. Web caching using access statistics. In Symposium on Discrete Algorithms, pages , H. Shachnai and T. Tamir. On Two Classconstrained Versions of the Multiple Knapsack Problem. Algorithmica, 29: ,
14 17. H. Shachnai and T. Tamir. Polynomial Time Approximation Schemes for Classconstrained Packing Problems. Workshop on Approximation Algorithms, LNCS 1913, pp , C.E. Shannon. A Theorem on Colouring Lines of a Network. J. Math. Phys., 28: , D.B. Shmoys and E. Tardos. An Aproximation Algorithm for the Generalized Assignment Problem. Mathematical Programming, A 62, pp , V. G. Vizing. On an Estimate of the Chromatic Class of a pgraph (Russian). Diskret. Analiz. 3:25 30,
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